arXiv cs.AI· Pepijn Cobben, Xuanqiang Angelo Huang, Thao Amelia Pham, Isabel Dahlgren, Bernhard Sch\"olkopf, Terry Jingchen Zhang, Zhijing Jin·· 1 天前AI 评分44
GT-HarmBench：用博弈论视角评测 AI 安全风险
GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory
AI 导读
研究者推出 GT-HarmBench，包含 1,535 个源自 MIT AI Risk Repository 的高风险场景，覆盖囚徒困境、猎鹿博弈和胆小鬼博弈等博弈论结构。在 15 个前沿模型上，智能体在 39% 的高风险案例中未能选择对社会有益的行动，涉及军事升级、选举操纵和医疗过失等情境。博弈论干预可将社会有益结果提升最多 31%。
正文 · 原文
Abstract:Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving multi-agent risks such as coordination failure and conflict poorly understood. We introduce GT-HarmBench, a benchmark of 1,535 high-stakes scenarios spanning game-theoretic structures such as the Prisoner's Dilemma, Stag Hunt and Chicken. Scenarios are drawn from realistic AI risk contexts in the MIT AI Risk Repository. Across 15 frontier models, agents fail to choose socially beneficial actions in 39% of high-stakes cases, such as military escalation, election manipulation, and medical malpractice. We measure sensitivity to game-theoretic prompt framing and ordering, and analyze reasoning patterns driving failures. We further show that game-theoretic interventions improve socially beneficial outcomes by up to 31%. Our results highlight substantial reliability gaps and provide a broad standardized testbed for studying alignment in multi-agent environments. The benchmark and code are available at this https URL.
| Comments: | Accepted at NeurIPS 2026 Main Conference |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2602.12316 [cs.AI] |
| (or arXiv:2602.12316v4 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2602.12316 arXiv-issued DOI via DataCite |
Submission history
From: Xuanqiang Angelo Huang [view email]
[v1]
Thu, 12 Feb 2026 17:29:52 UTC (4,343 KB)
[v2]
Fri, 22 May 2026 00:32:35 UTC (4,564 KB)
[v3]
Fri, 25 Sep 2026 14:05:39 UTC (4,564 KB)
[v4]
Wed, 11 Nov 2026 14:48:53 UTC (4,917 KB)
来源：arXiv cs.AI · arxiv.org